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SAR图像中舰船目标恒虚警率检测技术的研究

孟祥伟

孟祥伟. SAR图像中舰船目标恒虚警率检测技术的研究[J]. 电子与信息学报. doi: 10.11999/JEIT231436
引用本文: 孟祥伟. SAR图像中舰船目标恒虚警率检测技术的研究[J]. 电子与信息学报. doi: 10.11999/JEIT231436
MENG Xiangwei. Research on Constant False Alarm Rate Detection Technique for Ship in SAR Image[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231436
Citation: MENG Xiangwei. Research on Constant False Alarm Rate Detection Technique for Ship in SAR Image[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231436

SAR图像中舰船目标恒虚警率检测技术的研究

doi: 10.11999/JEIT231436
基金项目: 国家自然科学基金(62171402)
详细信息
    作者简介:

    孟祥伟:男,教授、博导,研究方向为雷达信号检测理论

    通讯作者:

    孟祥伟 mengxw163@sina.com

  • 中图分类号: TN957.51

Research on Constant False Alarm Rate Detection Technique for Ship in SAR Image

Funds: The National Natural Science Foundation of China (62171402)
  • 摘要: 在各种各样的合成孔径雷达图像舰船目标检测方法中,应用最广泛、最重要的就是具有自适应阈值的恒虚警率(CFAR)检测器。为了提高SAR图像中舰船目标的检测性能,人们试图通过各种统计分布模型对SAR图像中的杂波背景进行统计建模,如Gamma分布、K分布、对数正态分布、G0分布、alpha稳定分布等,再通过相应的统计分布模型以及各种样本筛选技术的CFAR检测器对舰船目标实施检测。SAR图像中杂波背景是复杂多变的,当实际杂波背景与假定统计分布失配时,参量型CFAR检测器的性能会恶化,非参数CFAR检测器就会显示出优势。该文提出了基于Wilcoxon非参数检测器的新途径对SAR图像中舰船目标进行检测,并在Radarsat-2, ICEYE-X6和Gaofen-3卫星的实测数据上,与几种典型的参量型CFAR检测方法进行了对比。实验结果表明,Wilcoxon非参数检测方法在这3种实测数据上的虚警控制能力具有良好的鲁棒性,还可以带来弱目标检测性能的改善,具有运算速度快、易于硬件实现的特点。
  • 图  1  Wilcoxon 非参数检测器的参考滑窗

    图  2  Radarsat-2的SAR 图像切片及几种检测器的检测结果

    图  3  几种检测器对图2(a)中白色圆圈中舰船目标检测结果的切片

    图  4  ICEYE-X6的SAR 图像切片及几种检测器的检测结果

    图  5  几种检测器对图4(a)中白色圆圈中舰船目标检测结果的切片

    图  6  Gaofen-3的SAR 图像切片及几种检测器的检测结果

    图  7  几种检测器对图6(a)中红色圆圈中弱舰船目标检测结果的切片

    图  8  几种检测器对弱舰船目标检测的ROC曲线

    图  9  Wilcoxon非参数检测器在检测单元t×t=3×3时的检测结果

    图  10  Wilcoxon检测器在t×t=3×3时对舰船目标的检测结果切片

    表  1  几种SAR图像舰船目标CFAR检测方法虚警性能的比较

    双参数CFAR 韦布尔-CFAR TS-CFAR AIS-RCFAR Wilcoxon方法
    图2(a)所示SAR图像 Nfa 102 106 122 118 26
    Nc 900 318 900 318 900 318 900 318 900 318
    Pfa(×10–4) 1.13 1.18 1.36 1.31 0.29
    PFA 1.0×10–7 3.0×10–5 1.0×10–4 3.0×10–6 1.0×10–8
    运算时间Ts(s) 30.86 311.17 382.35 66.50 12.83
    图4(a)所示SAR图像 Nfa 223 219 266 213 108
    Nc 2 481 960 2 481 960 2 481 960 2 481 960 2 481 960
    Pfa(×10–4) 0.90 0.88 1.07 0.86 0.44
    PFA 1.0×10–8 1.0×10–5 3.0×10–4 1.0×10–6 1.0×10–8
    运算时间Ts(s) 86.34 944.72 1196.44 546.77 56.54
    图6(a)所示SAR图像 Nfa 137 136 134 136 90
    Nc 1 320 909 1 320 909 1 320 909 1 320 909 1 320 909
    Pfa(×10–4) 1.04 1.03 1.01 1.03 0.68
    PFA 1.0×10–11 3.0×10–8 3.0×10–5 4.0×10–7 1.0×10–8
    运算时间Ts(s) 47.30 485.04 644.80 393.23 118.68
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-28
  • 修回日期:  2024-06-13
  • 网络出版日期:  2024-06-19

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